4.4k citations · 7k across the 47 of their papers we have counts for
49 papers
An Empirical Study of World Model Quantization
Zhongqian Fu, Tianyi Zhao, Kai Han +3
World models learn an internal representation of environment dynamics, enabling agents to simulate and reason about future states within a compact latent space for tasks such as pl…
Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Large Vision-Language Models
Chengcheng Wang, Jianyuan Guo, Hongguang Li +4
Rotary Position Embedding (RoPE) is widely adopted in large language models, but when applied to vision-language models (VLMs) it couples text and image position indices and can in…
GhostRNN: Reducing State Redundancy in RNN with Cheap Operations
Hang Zhou, Xiaoxu Zheng, Yunhe Wang +3
Recurrent neural network (RNNs) that are capable of modeling long-distance dependencies are widely used in various speech tasks, eg., keyword spotting (KWS) and speech enhancement…
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models
Ying Nie, Wei He, Kai Han +4
Vision-language pre-training like CLIP has shown promising performance on various downstream tasks such as zero-shot image classification and image-text retrieval. Most of the exis…
One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation
Zhiwei Hao, Jianyuan Guo, Kai Han +4
Knowledge distillation~(KD) has proven to be a highly effective approach for enhancing model performance through a teacher-student training scheme. However, most existing distillat…
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
Chengcheng Wang, Wei He, Ying Nie +4
In the past years, YOLO-series models have emerged as the leading approaches in the area of real-time object detection. Many studies pushed up the baseline to a higher level by mod…
Category Feature Transformer for Semantic Segmentation
Quan Tang, Chuanjian Liu, Fagui Liu +5
Aggregation of multi-stage features has been revealed to play a significant role in semantic segmentation. Unlike previous methods employing point-wise summation or concatenation f…
ParameterNet: Parameters Are All You Need
Kai Han, Yunhe Wang, Jianyuan Guo +1
The large-scale visual pretraining has significantly improve the performance of large vision models. However, we observe the \emph{low FLOPs pitfall} that the existing low-FLOPs mo…
VanillaKD: Revisit the Power of Vanilla Knowledge Distillation from Small Scale to Large Scale
Zhiwei Hao, Jianyuan Guo, Kai Han +3
The tremendous success of large models trained on extensive datasets demonstrates that scale is a key ingredient in achieving superior results. Therefore, the reflection on the rat…
Masked Image Modeling with Local Multi-Scale Reconstruction
Haoqing Wang, Yehui Tang, Yunhe Wang +3
Masked Image Modeling (MIM) achieves outstanding success in self-supervised representation learning. Unfortunately, MIM models typically have huge computational burden and slow lea…
Redistribution of Weights and Activations for AdderNet Quantization
Ying Nie, Kai Han, Haikang Diao +3
Adder Neural Network (AdderNet) provides a new way for developing energy-efficient neural networks by replacing the expensive multiplications in convolution with cheaper additions…
FastMIM: Expediting Masked Image Modeling Pre-training for Vision
Jianyuan Guo, Kai Han, Han Wu +3
The combination of transformers and masked image modeling (MIM) pre-training framework has shown great potential in various vision tasks. However, the pre-training computational bu…
GhostNetV2: Enhance Cheap Operation with Long-Range Attention
Yehui Tang, Kai Han, Jianyuan Guo +3
Light-weight convolutional neural networks (CNNs) are specially designed for applications on mobile devices with faster inference speed. The convolutional operation can only captur…
Vision GNN: An Image is Worth Graph of Nodes
Kai Han, Yunhe Wang, Jianyuan Guo +2
Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or s…
GhostNets on Heterogeneous Devices via Cheap Operations
Kai Han, Yunhe Wang, Chang Xu +4
Deploying convolutional neural networks (CNNs) on mobile devices is difficult due to the limited memory and computation resources. We aim to design efficient neural networks for he…
PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture
Kai Han, Jianyuan Guo, Yehui Tang +1
Transformer networks have achieved great progress for computer vision tasks. Transformer-in-Transformer (TNT) architecture utilizes inner transformer and outer transformer to extra…
An Image Patch is a Wave: Phase-Aware Vision MLP
Yehui Tang, Kai Han, Jianyuan Guo +4
In the field of computer vision, recent works show that a pure MLP architecture mainly stacked by fully-connected layers can achieve competing performance with CNN and transformer.…
Learning Versatile Convolution Filters for Efficient Visual Recognition
Kai Han, Yunhe Wang, Chang Xu +3
This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the demands of ef…
Hire-MLP: Vision MLP via Hierarchical Rearrangement
Jianyuan Guo, Yehui Tang, Kai Han +5
Previous vision MLPs such as MLP-Mixer and ResMLP accept linearly flattened image patches as input, making them inflexible for different input sizes and hard to capture spatial inf…
Greedy Network Enlarging
Chuanjian Liu, Kai Han, An Xiao +4
Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i.e., models with more MACs typically achieve better accuracy, such as Effici…
CMT: Convolutional Neural Networks Meet Vision Transformers
Jianyuan Guo, Kai Han, Han Wu +4
Vision transformers have been successfully applied to image recognition tasks due to their ability to capture long-range dependencies within an image. However, there are still gaps…
Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation
Zhiwei Hao, Jianyuan Guo, Ding Jia +5
In the past few years, transformers have achieved promising performances on various computer vision tasks. Unfortunately, the immense inference overhead of most existing vision tra…
Augmented Shortcuts for Vision Transformers
Yehui Tang, Kai Han, Chang Xu +4
Transformer models have achieved great progress on computer vision tasks recently. The rapid development of vision transformers is mainly contributed by their high representation a…
Post-Training Quantization for Vision Transformer
Zhenhua Liu, Yunhe Wang, Kai Han +2
Recently, transformer has achieved remarkable performance on a variety of computer vision applications. Compared with mainstream convolutional neural networks, vision transformers…